def partNum(z, pxlen, R_mu, R_sigma):
A = (np.shape(z)[1]-1)*(np.shape(z)[0]-1) * pxlen**2
V = par.V(z, pxlen)
V_mean = 4/3 * np.pi * np.exp(3*R_mu + 3**2*R_sigma**2/2)
N_particles = V / V_mean
eff_cov = N_particles / A
return N_particles, eff_cov
def partNum(z, pxlen, R_mu, R_sigma):
A = len(z)**2 * pxlen**2
V = par.V(z, pxlen)
V_mean = 4 / 3 * np.pi * np.exp(3 * R_mu + 3**2 * R_sigma**2 / 2)
N_part = V / V_mean
eff_cov = N_part / A
return N_part, eff_cov
def plotVfracDep(Npx, pxlen, N_part_max, N_part_step, R_mean, R_std, R_mu,
R_sigma):
z = mf.genFlat(Npx)
V_real = 0
V_frac_list = []
N_part = np.arange(0, N_part_max + 1, N_part_step)
for N in N_part:
z, R_part_real = mf.genLogNormSolidSph(z, pxlen, N_part_step, R_mean,
R_std)
V_real += np.sum(4 / 3 * np.pi * R_part_real**3)
V_frac_list.append(par.V(z, pxlen) / V_real)
print(N)
plt.figure()
plt.plot(N_part, V_frac_list, color='r', marker='o')
plt.xlabel(r'$N_{part,real}$')
plt.ylabel(r'$V_{tot,est} / V_{tot,real}$')
plt.title(r'$\mu_R = $' + str(R_mu) + r'$nm, \sigma_R = $' + str(R_sigma) +
r'$nm$' + r'$, R_{mean} = $' + str(R_mean) +
r'$nm, R_{std} = $' + str(R_std) + r'$nm$')
plt.grid()
def partDep(Npx, pxlen, step_sim, N_part_min, N_part_max, N_part_step, R_mu, R_sigma, firstmap='', usefile=False, savefile=False):
N_part = np.linspace(np.log10(N_part_min), np.log10(N_part_max), N_part_step)
N_part = np.round(10**N_part)
N_part.astype(int, copy=False)
# N_est = np.array([]) #2+1D only
V_est = np.array([])
rms_est = np.array([])
h_est = np.array([])
h_top = np.array([])
C_true=[]
G_true=[]
C2_true=[]
G2_true=[]
C_list=[]
G_list=[]
for i in range(step_sim):
if firstmap=='': #initialize map
#z = np.zeros(Npx) #1+1D
z=mf.genFlat(Npx)
N_prec=0
V_real=0
else:
z = np.loadtxt(firstmap)
dotpos=firstmap.find('.')
start=dotpos-1
for i in range(dotpos-1):
if firstmap[start]=='_': break
start-=1
N_prec=int(firstmap[start+1:dotpos])
out=open(firstmap)
head=out.readline()
out.close()
start=head.find('V=')+2
V_real=float(head[start:head.find(';', start)])
for N in N_part:
print('Sim.',i+1,'; N=',str(N)[:len(str(N))-2], end=' ')
if usefile and isfile('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat'):
print('map from file ...', end=' ')
z= np.loadtxt('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat')
out=open('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat')
head=out.readline()
out.close()
start=head.find('V=')+2
V_real=float(head[start:head.find(';', start)])
else:
#print('generating map ...', end=' ')
#z, R_part_real = mf.genLogNormSolidSph(z,pxlen,int(N-N_prec),R_mu,R_sigma)
#z=mf.genLattice1d(z,pxlen,int(N-N_prec)) #1+1D
#V_real += (N-N_prec)* pxlen**2 #1+1D
z=mf.genLattice2d(z,pxlen,int(N-N_prec))
V_real += (N-N_prec)* pxlen**3
#V_real += 4/3*np.pi * np.sum(R_part_real**3)
if savefile: np.savetxt('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat', z, header='V='+str(V_real)+'; Npx,pxlen,Npart in filename')
#if savefile: np.savetxt('matrice.mat', z)
N_prec=N
#print('computing parameters ...')
#N_est=np.append(N_est, partNum(z,pxlen,R_mu,R_sigma)[0]/N)
V_est=np.append(V_est, par.V(z,pxlen)/V_real)
rms_est=np.append(rms_est, np.std(z))
h_est=np.append(h_est, np.mean(z))
h_top=np.append(h_top, np.amax(z))
#print('computing correlations ...')
l,C=par.C_profile(z, pxlen, 1)
C_list.append(C)
#C_list.append(par.C_1d(z)) #1+1D
l,C=par.G_profile(z, pxlen, 1)
G_list.append(C)
#G_list.append(par.G_1d(z)) #1+1D
print('',end='\r')
#print()
if i==0:
for el in C_list:
C_true.append(el)
C2_true.append(el**2)
for el in G_list:
G_true.append(el)
G2_true.append(el**2)
else:
for i in range(len(C_true)):
C_true[i]=C_true[i]+C_list[i]
C2_true[i]=C2_true[i]+C_list[i]**2
for i in range(len(G_true)):
G_true[i]=G_true[i]+G_list[i]
G2_true[i]=G2_true[i]+G_list[i]**2
C_list.clear()
G_list.clear()
filename=['V_relVsN.dat', 'rmsVsN.dat', 'hVsN.dat', 'maxhVsN.dat']
est=[V_est, rms_est, h_est, h_top]
for j in range(len(est)):
err=np.array([])
for i in range(len(N_part)):
if step_sim==1: err=np.append(err, 0)
else: err=np.append(err, np.std(est[j][i::len(N_part)]))
est[j][i]=np.mean(est[j][i::len(N_part)])
np.savetxt(filename[j], np.array([ est[j][:len(N_part)], N_part, err ]),
header=str(Npx) + ' ' + str(pxlen) + '\n' +
r'$N_{px}$ $L_{px}$')
#header=str(R_mu) + ' ' + str(R_sigma) + ' ' + str(Npx) + ' ' + str(pxlen) + '\n' +
#r'$\mu_R$ $\sigma_R$ $N_{px}$ $L_{px}$')
print('data saved in '+ filename[j] +' and in folder maps/')
C_true=np.array(C_true)/step_sim
G_true=np.array(G_true)/step_sim
C2_true=np.array(C2_true)/step_sim - C_true**2
G2_true=np.array(G2_true)/step_sim - G_true**2
# np.savetxt('x.dat', l)
np.savetxt('C.dat', C_true)
np.savetxt('G.dat', G_true)
np.savetxt('C2.dat', C2_true)
np.savetxt('G2.dat', G2_true)
print('correlations saved in files C,G,C2,G2')
def partDep_mpi(Npx, pxlen, step_sim, N_part_min, N_part_max, N_part_step, R_mu, R_sigma, firstmap='', usefile=False, savefile=False):
N_part = np.linspace(np.log10(N_part_min), np.log10(N_part_max), N_part_step)
N_part = np.round(10**N_part)
N_part.astype(int, copy=False)
N_est = np.array([])
V_est = np.array([])
rms_est = np.array([])
h_est = np.array([])
h_top = np.array([])
# L_corr_est = np.array([])
# L_corr_err = np.array([])
# alfa_est=np.array([])
# alfa_err=np.array([])
C_true=[]
G_true=[]
C2_true=[]
G2_true=[]
C_list=[]
G_list=[]
comm=MPI.COMM_WORLD
size_mpi=comm.Get_size()
for i in range(step_sim/size_mpi):
if firstmap=='': #initialize map
z = mf.genFlat(Npx)
N_prec=0
V_real=0
else:
z = np.loadtxt(firstmap)
dotpos=firstmap.find('.')
start=dotpos-1
for i in range(dotpos-1):
if firstmap[start]=='_': break
start-=1
N_prec=int(firstmap[start+1:dotpos])
out=open(firstmap)
head=out.readline()
out.close()
start=head.find('V=')+2
V_real=float(head[start:head.find(';', start)])
for N in N_part:
print('Sim.',i+1,'; N=',str(N)[:len(str(N))-2], end=' ')
if usefile and isfile('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat'):
print('map from file ...', end=' ')
z= np.loadtxt('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat')
out=open('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat')
head=out.readline()
out.close()
start=head.find('V=')+2
V_real=float(head[start:head.find(';', start)])
else:
print('generating map ...', end=' ')
z, R_part_real = mf.genLogNormSolidSph(z,pxlen,int(N-N_prec),R_mu,R_sigma)
mf.plotfalsecol(z,pxlen)
V_real += np.sum(4/3 * np.pi * R_part_real**3)
if savefile: np.savetxt('maps/lognorm_'+str(Npx)+'_'+str(pxlen)+'_'+str(N)[:len(str(N))-2]+'.dat', z, header='V='+str(V_real)+'; Npx,pxlen,Npart in filename')
N_prec=N
print('computing parameters ...')
N_est=np.append(N_est, partNum(z,pxlen,R_mu,R_sigma)[0]/N)
V_est=np.append(V_est, par.V(z,pxlen)/V_real)
rms_est=np.append(rms_est, np.std(z))
h_est=np.append(h_est, np.mean(z))
h_top=np.append(h_top, np.amax(z))
#print('computing correlations ...')
l,C=par.C_profile(z, 2, 800)
C_list.append(C)
l,C=par.G_profile(z, 2, 800)
G_list.append(C)
if i==0:
for el in C_list:
C_true.append(el)
C2_true.append(el**2)
for el in G_list:
G_true.append(el)
G2_true.append(el**2)
else:
for i in range(len(C_true)):
C_true[i]=C_true[i]+C_list[i]
C2_true[i]=C2_true[i]+C_list[i]**2
for i in range(len(G_true)):
G_true[i]=G_true[i]+G_list[i]
G2_true[i]=G2_true[i]+G_list[i]**2
C_list.clear()
G_list.clear()
est=[N_est, V_est, rms_est, h_est, h_top]
rank=comm.Get_rank()
if rank!=0: comm.Send(est, dest=0)
if rank==0:
rec=[]
for i in range(1, size_mpi):
comm.Recv(rec, source=i)
for k in range(len(est)):
est[k]=np.append(est[k], rec[k])
rec.clear()
C_true=np.array(C_true)/(step_sim/size_mpi)
G_true=np.array(G_true)/(step_sim/size_mpi)
C2_true=np.array(C2_true)/(step_sim/size_mpi) - C_true**2
G2_true=np.array(G2_true)/(step_sim/size_mpi) - G_true**2
corr=[C_true, G_true, C2_true, G2_true]
if rank!=0: comm.Send(corr, dest=0)
if rank==0:
for i in range(1, size_mpi):
comm.Recv(rec, source=i)
for k in range(4):
corr[k]=np.append(corr[k], rec[k])
rec.clear()
corr[0]=np.array(corr[0])/size_mpi
corr[1]=np.array(corr[1])/size_mpi
corr[2]=np.array(corr[2])/size_mpi - corr[0]**2
corr[3]=np.array(corr[3])/size_mpi - corr[1]**2
filename=['N_relVsN.dat', 'V_relVsN.dat', 'rmsVsN.dat', 'hVsN.dat', 'maxhVsN.dat']
for j in range(len(est)):
err=np.array([])
for i in range(len(N_part)):
if step_sim==1: err=np.append(err, 0)
else: err=np.append(err, np.std(est[j][i::len(N_part)]))
est[j][i]=np.mean(est[j][i::len(N_part)])
np.savetxt(filename[j], np.array([ est[j][:len(N_part)], N_part, err ]),
header=str(R_mu) + ' ' + str(R_sigma) + ' ' + str(Npx) + ' ' + str(pxlen) + '\n' +
r'$\mu _R$ $\sigma _R$ $N_{px}$ $L_{px}$')
print('data saved in '+ filename[j] +' and in folder maps/')
np.savetxt('correlations.dat', np.array([ l*pxlen, corr[0], corr[1], corr[2], corr[3]]), fmt='%s')
pxlen=L/Npx
thres=0 #soglia
#-----------------------------------------------
R=q*L
htip=2*R*1.02
R_tip=np.linspace(R/5,3*R, 8)
V_red_dil = []
height = []
profiles = []
posmax = []
z=mf.genFlat(int(Npx[-1]))
z=mf.genSphere(z,pxlen[-1],np.array([L/2, L/2]),np.array([R]))
obj = mf.identObj(z,thres)[0]
V_red_calc= par.V(obj, pxlen[-1]) / volsphere(R)
maxh=0
posmax.append(0)
for x in range(np.shape(obj)[0]):
height.append(np.amax(obj[0:,x])/R)
if maxh<height[-1]:
maxh=height[-1]
posmax[-1]=x
profiles.append(np.array(height))
height.clear()
for rtip in R_tip:
for i in range(len(Npx)): #iterazione su risoluzione
print('R_tip=', rtip , ' Npx=', int(Npx[i]))
z=mf.genFlat(int(Npx[i]))
def partDep(Npx,
pxlen,
step_sim,
N_part_min,
N_part_max,
N_part_step,
R_mean,
R_std,
par,
firstmap='',
usefile=False,
savefile=False):
N_part = np.linspace(np.log10(N_part_min), np.log10(N_part_max),
N_part_step)
N_part = np.round(10**N_part)
N_part.astype(int, copy=False)
R_mu = np.log(R_mean /
np.sqrt(1 + R_std**2 / R_mean**2)) # recalculated gaussian
R_sigma = np.sqrt(np.log(1 +
(R_std / R_mean)**2)) # reculaculated gaussian
est_list = []
wl_list = []
for i in range(step_sim):
if firstmap == '': #initialize map
z = mf.genFlat(Npx)
N_prec = 0
V_real = 0
else:
z = np.loadtxt(firstmap)
dotpos = firstmap.find('.')
start = dotpos - 1
for i in range(dotpos - 1):
if firstmap[start] == '_': break
start -= 1
N_prec = int(firstmap[start + 1:dotpos])
out = open(firstmap)
head = out.readline()
out.close()
start = head.find('V=') + 2
V_real = float(head[start:head.find(';', start)])
for N in N_part:
print('Sim.', i + 1, 'N=', str(N)[:len(str(N)) - 2], end=' ')
if usefile and isfile('maps/lognorm_' + str(Npx) + '_' +
str(pxlen) + '_' + str(N)[:len(str(N)) - 2] +
'.dat'):
print('map from file ...')
z = np.loadtxt('maps/lognorm_' + str(Npx) + '_' + str(pxlen) +
'_' + str(N)[:len(str(N)) - 2] + '.dat')
out = open('maps/lognorm_' + str(Npx) + '_' + str(pxlen) +
'_' + str(N)[:len(str(N)) - 2] + '.dat')
head = out.readline()
out.close()
start = head.find('V=') + 2
V_real = float(head[start:head.find(';', start)])
else:
print('generating map ...')
z, R_part_real = mf.genLogNormSolidSph(z, pxlen,
int(N - N_prec), R_mean,
R_std)
V_real += np.sum(4 / 3 * np.pi * R_part_real**3)
if savefile:
np.savetxt('maps/lognorm_' + str(Npx) + '_' + str(pxlen) +
'_' + str(N)[:len(str(N)) - 2] + '.dat',
z,
header='V=' + str(V_real) +
'; Npx,pxlen,Npart in filename')
N_prec = N
if par == 'N' or par == 'N_part' or par == 'Npart':
est_list.append(partNum(z, pxlen, R_mu, R_sigma)[0])
if par == 'V' or par == 'V_rel' or par == 'V_frac':
est_list.append(par.V(z, pxlen) / V_real)
if par == 'rms' or par == 'RMS' or par == 'std':
est_list.append(np.std(z))
wl_list.append(par.wavelength(z, pxlen, 'x'))
if par == 'N' or par == 'N_part' or par == 'Npart':
filename = 'N_relVsN_real.dat'
if par == 'V' or par == 'V_rel' or par == 'V_frac':
filename = 'V_relVsV_real.dat'
if par == 'rms' or par == 'RMS' or par == 'std':
filename = 'rmsVsN_real.dat'
err = []
for i in range(len(N_part)):
if step_sim == 1: err.append(0)
else: err.append(np.std(np.array(est_list[i::len(N_part)])))
est_list[i] = np.mean(np.array(est_list[i::len(N_part)]))
wl_list[i] = np.mean(np.array(wl_list[i::len(N_part)]))
np.savetxt(filename,
np.array(
[np.array(est_list[:len(N_part)]), N_part,
np.array(err)], np.array(wl_list[:len(N_part)])),
header=str(R_mu) + ' ' + str(R_sigma) + ' ' + str(R_mean) +
' ' + str(R_std) + ' ' + str(Npx) + ' ' + str(pxlen) + '\n' +
r'$\mu_R$ $\sigma_R$ $R_{mean}$ $R_{std}$ $N_{px}$ $L_{px}$' +
'\n wavelength on last line')
print('data saved in ' + filename + 'and in folder maps/')
